DocumentCode
238722
Title
An algorithm for scalable clustering: Ensemble Rapid Centroid Estimation
Author
Yuwono, Mitchell ; Sir, Steven W. ; Moulton, Brace D. ; Ying Guo ; Nguyen, Hung T.
Author_Institution
Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
fYear
2014
fDate
6-11 July 2014
Firstpage
1250
Lastpage
1257
Abstract
This paper describes a new algorithm, called Ensemble Rapid Centroid Estimation (ERCE), designed to handle large-scale non-convex cluster optimization tasks, and estimate the number of clusters with quasi-linear complexity. ERCE stems from a recently developed Rapid Centroid Estimation (RCE) algorithm. RCE was originally developed as a lightweight simplification of the Particle Swarm Clustering (PSC) algorithm. RCE retained the quality of PSC, greatly reduced the computational complexity, and increased the stability. However, RCE has certain limitations with respect to complexity, and is unsuitable for non-convex clusters. The new ERCE algorithm presented here addresses these limitations.
Keywords
computational complexity; concave programming; particle swarm optimisation; pattern clustering; ERCE algorithm; PSC algorithm; cluster number estimation; computational complexity reduction; ensemble rapid centroid estimation algorithm; large-scale nonconvex cluster optimization tasks; particle swarm clustering algorithm; quasilinear complexity; scalable clustering algorithm; Algorithm design and analysis; Clustering algorithms; Computational complexity; Estimation; Indexes; Particle swarm optimization; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location
Beijing
Print_ISBN
978-1-4799-6626-4
Type
conf
DOI
10.1109/CEC.2014.6900295
Filename
6900295
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